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Functionality: RNA

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Hans-Joachim Ruscheweyh edited this page Jul 10, 2017 · 6 revisions

Here we describe the functionality included in NGS-pipe to analyse DNA experiments. The figure depicted below provides an schematic overview of the general categories we used to cluster the rules provided in NGS-pipe.

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For each rule we give a short description if its functionality deviates from the cluster description.

Adapter removal and quality trimming

Sometimes the raw sequences contain adapters which need to be removed in order to improve accuracy of later analysis steps. Furthermore, we also remove low quality bases from the end of the sequences. Two rules are in charge of the raw sequence clipping and trimming:

  • rule trimSingle, trimPaired: Trimmomatic (Bolger 2014)

Alignment

The raw sequencing files need to be mapped to a reference sequence in order to determine their point of origin. We use the STAR (Dobin 2013) aligner for this. The rule takes multiple FASTQ files and aligns and merges their content:

  • rule alignSingleFileStar, alignPairedFileStar: STAR (Dobin 2013)

Gene/Feature Counting

Aligned sequences are then screened for genes that fall onto the same location as the reads and quantified. For this we use the tool featureCounts (Liao 2013) that comes as a part of the subread package. The output of this tool is then translated to the typical HTSeq (Anders 2010) output format:

  • rule featurecountsSingle, featurecountsPaired: (Liao 2013)
  • rule featurecounts2HtSeq: (snake/rna/fcnt2htseq.py)

Run Statistics

For every step in the analysis we collect certain standard statistics that users can use to make informed decisions on the quality of the outcome. We run samtools (Li 2009) flagstats and samtools header on all generated BAM files. FASTQC (Andrews 2010) and a self written sequences count script is executed on every FASTQ file:

  • rule runFlagstat, runSamtoolsHeader: (Li 2009)
  • rule runFastqcOnFastqs: (Andrews 2010)
  • rule countReadsInFastq: (in snake/rna/rna_snake.py)

References

Anders, S., Pyl, P. T., & Huber, W. (2010). HTSeq: Analysing high-throughput sequencing data with Python.

Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data.

Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(4), 2114-2120.

Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., ... & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15-21.

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., ... & Durbin, R. (2009). The sequence alignment/map format and SAMtools. Bioinformatics, 25(16), 2078-2079.

Liao, Y., Smyth, G. K., & Shi, W. (2013). featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923-930.

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